The limitations of the professional knowledge and cognitive capabilities of both attackers and defenders mean that moving target attack-defense conflicts are not completely rational, which makes it difficult to select optimal moving target defense strategies difficult for use in real-world attack-defense scenarios. Starting from the imperfect rationality of both attack-defense, we construct a Wright-Fisher process-based moving target defense strategy evolution model called WF-MTD. In our method, we introduce rationality parameters to describe the strategy learning capabilities of both the attacker and the defender. By solving for the evolutionarily stable equilibrium, we develop a method for selecting the optimal defense strategy for moving targets and describe the evolution trajectories of the attack-defense strategies. Our experimental results in our example of a typical network information system show that WF-MTD selects appropriate MTD strategies in different states along different attack paths, with good effectiveness and broad applicability. In addition, compared with no hopping strategy, fixed periodic route hopping strategy, and random periodic route hopping strategy, the route hopping strategy based on WF-MTD increase defense payoffs by 58.7%, 27.6%, and 24.6%, respectively.
The centralized control characteristics of software-defined networks (SDNs) make them susceptible to advanced persistent threats (APTs). Moving target defense, as an effective defense means, is constantly developing. It is difficult to effectively characterize an MTD attack and defense game with existing game models and effectively select the defense timing to balance SDN service quality and MTD decision-making benefits. From the hidden confrontation between the actual attack and defense sides, existing attack-defense scenarios are abstractly characterized and analyzed. Based on the APT attack process of the Cyber Kill Chain (CKC), a state transition model of the MTD attack surface based on the susceptible-infective-recuperative-malfunctioned (SIRM) infectious disease model is defined. An MTD attack-defense timing decision model based on the FlipIt game (FG-MTD) is constructed, which expands the static analysis in the traditional game to a dynamic continuous process. The Nash equilibrium of the proposed method is analyzed, and the optimal timing selection algorithm of the MTD is designed to provide decision support for the selection of MTD timing under moderate security. Finally, the application model is used to verify the model and method. Through numerical analysis, the timings of different types of attack-defense strategies are summarized.
Background:
SLAM plays an important role in the navigation of robots, unmanned aerial vehicles, and unmanned vehicles. The positioning accuracy will affect the accuracy of obstacle avoidance. The quality of map construction directly affects the performance of subsequent path planning and other algorithms. It is the core algorithm of the intelligent mobile application. Therefore, robot vision slam has great research value and will be an important research direction in the future.
Objective:
By reviewing the latest development and patent of Computer Vision SLAM, this paper provides references to researchers in related fields.
Method:
Computer Vision SLAM patents and literature were analyzed from the aspects of the algorithm, innovation, and application. Among them, there are more than 30 patents and nearly 30 pieces of literature in the past ten years.
Results:
This paper reviews the research progress of robot visual SLAM in the last 10 years, summarizes its typical features, especially describes the front part of the visual SLAM system in detail, describes the main advantages and disadvantages of each method, analyses the main problems in the development of robot visual SLAM, prospects its development trend, and finally discusses the related products and patents research status and future of robot visual SLAM technology.
Conclusion:
The Robot Vision SLAM can compare the texture information of the environment and identify the difference between the two environments, thus improving accuracy. However, the current SLAM algorithm is easy to fail in fast motion and highly dynamic environments, most SLAM action plans are inefficient, and the image features of VSLAM are too distinguishable. Furthermore, more patents on the Robot Vision SLAM should also be invented.
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